#ifndef STAN_MATH_PRIM_SCAL_PROB_BETA_LCDF_HPP
#define STAN_MATH_PRIM_SCAL_PROB_BETA_LCDF_HPP

#include <stan/math/prim/scal/meta/is_constant_struct.hpp>
#include <stan/math/prim/scal/meta/partials_return_type.hpp>
#include <stan/math/prim/scal/meta/operands_and_partials.hpp>
#include <stan/math/prim/scal/err/check_consistent_sizes.hpp>
#include <stan/math/prim/scal/err/check_less_or_equal.hpp>
#include <stan/math/prim/scal/err/check_nonnegative.hpp>
#include <stan/math/prim/scal/err/check_not_nan.hpp>
#include <stan/math/prim/scal/err/check_positive_finite.hpp>
#include <stan/math/prim/scal/fun/log1m.hpp>
#include <stan/math/prim/scal/fun/multiply_log.hpp>
#include <stan/math/prim/scal/fun/value_of.hpp>
#include <stan/math/prim/scal/fun/digamma.hpp>
#include <stan/math/prim/scal/fun/lgamma.hpp>
#include <stan/math/prim/scal/fun/lbeta.hpp>
#include <stan/math/prim/scal/meta/contains_nonconstant_struct.hpp>
#include <stan/math/prim/scal/fun/constants.hpp>
#include <stan/math/prim/scal/meta/include_summand.hpp>
#include <stan/math/prim/scal/meta/scalar_seq_view.hpp>
#include <stan/math/prim/scal/meta/VectorBuilder.hpp>
#include <stan/math/prim/scal/fun/grad_reg_inc_beta.hpp>
#include <stan/math/prim/scal/fun/inc_beta.hpp>
#include <boost/math/special_functions/gamma.hpp>
#include <boost/random/gamma_distribution.hpp>
#include <boost/random/variate_generator.hpp>
#include <cmath>
#include <string>

namespace stan {
  namespace math {

    /**
     * Returns the beta log cumulative distribution function for the given
     * probability, success, and failure parameters. Given matching containers
     * returns the log sum of probabilities.
     *
     * @tparam T_y type of probability parameter
     * @tparam T_scale_succ type of success parameter
     * @tparam T_scale_fail type of failure parameter
     * @param y probability parameter
     * @param alpha success parameter
     * @param beta failure parameter
     * @return log probability or log sum of probabilities
     * @throw std::domain_error if alpha or beta is negative
     * @throw std::domain_error if y is not a valid probability
     * @throw std::invalid_argument if container sizes mismatch
     */
    template <typename T_y, typename T_scale_succ, typename T_scale_fail>
    typename return_type<T_y, T_scale_succ, T_scale_fail>::type
    beta_lcdf(const T_y& y, const T_scale_succ& alpha,
                 const T_scale_fail& beta) {
      typedef typename stan::partials_return_type<T_y, T_scale_succ,
                                                  T_scale_fail>::type
        T_partials_return;

      if (!(stan::length(y) && stan::length(alpha) && stan::length(beta)))
        return 0.0;

      static const std::string function = "beta_lcdf";

      using boost::math::tools::promote_args;

      T_partials_return cdf_log(0.0);

      check_positive_finite(function, "First shape parameter", alpha);
      check_positive_finite(function, "Second shape parameter", beta);
      check_not_nan(function, "Random variable", y);
      check_nonnegative(function, "Random variable", y);
      check_less_or_equal(function, "Random variable", y, 1);
      check_consistent_sizes(function,
                             "Random variable", y,
                             "First shape parameter", alpha,
                             "Second shape parameter", beta);

      scalar_seq_view<T_y> y_vec(y);
      scalar_seq_view<T_scale_succ> alpha_vec(alpha);
      scalar_seq_view<T_scale_fail> beta_vec(beta);
      size_t N = max_size(y, alpha, beta);

      operands_and_partials<T_y, T_scale_succ, T_scale_fail>
        ops_partials(y, alpha, beta);

      using std::pow;
      using std::exp;
      using std::log;
      using std::exp;

      VectorBuilder<contains_nonconstant_struct<T_scale_succ,
                                                T_scale_fail>::value,
                    T_partials_return, T_scale_succ, T_scale_fail>
        digamma_alpha_vec(max_size(alpha, beta));

      VectorBuilder<contains_nonconstant_struct<T_scale_succ,
                                                T_scale_fail>::value,
                    T_partials_return, T_scale_succ, T_scale_fail>
        digamma_beta_vec(max_size(alpha, beta));

      VectorBuilder<contains_nonconstant_struct<T_scale_succ,
                                                T_scale_fail>::value,
                    T_partials_return, T_scale_succ, T_scale_fail>
        digamma_sum_vec(max_size(alpha, beta));

      if (contains_nonconstant_struct<T_scale_succ, T_scale_fail>::value) {
        for (size_t i = 0; i < N; i++) {
          const T_partials_return alpha_dbl = value_of(alpha_vec[i]);
          const T_partials_return beta_dbl = value_of(beta_vec[i]);

          digamma_alpha_vec[i] = digamma(alpha_dbl);
          digamma_beta_vec[i] = digamma(beta_dbl);
          digamma_sum_vec[i] = digamma(alpha_dbl + beta_dbl);
        }
      }

      for (size_t n = 0; n < N; n++) {
        const T_partials_return y_dbl = value_of(y_vec[n]);
        const T_partials_return alpha_dbl = value_of(alpha_vec[n]);
        const T_partials_return beta_dbl = value_of(beta_vec[n]);
        const T_partials_return betafunc_dbl = exp(lbeta(alpha_dbl, beta_dbl));
        const T_partials_return Pn = inc_beta(alpha_dbl, beta_dbl, y_dbl);

        cdf_log += log(Pn);

        if (!is_constant_struct<T_y>::value)
          ops_partials.edge1_.partials_[n] += pow(1 - y_dbl, beta_dbl - 1)
            * pow(y_dbl, alpha_dbl - 1) / betafunc_dbl / Pn;

        T_partials_return g1 = 0;
        T_partials_return g2 = 0;

        if (contains_nonconstant_struct<T_scale_succ, T_scale_fail>::value) {
          grad_reg_inc_beta(g1, g2, alpha_dbl, beta_dbl, y_dbl,
                            digamma_alpha_vec[n],
                            digamma_beta_vec[n], digamma_sum_vec[n],
                            betafunc_dbl);
        }
        if (!is_constant_struct<T_scale_succ>::value)
          ops_partials.edge2_.partials_[n] += g1 / Pn;
        if (!is_constant_struct<T_scale_fail>::value)
          ops_partials.edge3_.partials_[n] += g2 / Pn;
      }

      return ops_partials.build(cdf_log);
    }

  }
}
#endif
